Easy to use Audio Tagging in PyTorch

Related tags

Deep Learningaudiopytorchaudio-classificationsound-event-detectionaudio-taggingcnn14
Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Progress:

  • Fine-tune on audio classification
  • Fine-tune on audio tagging
  • Fine-tune on sound event detection
  • Add tagging metrics
  • Add Tutorial
  • Add Augmentation Notebook
  • Add more schedulers
  • Add FSDKaggle2019 dataset
  • Add MTT dataset
  • Add DESED

Model Zoo

AudioSet Pretrained Models
Model Task mAP
(%)		 Sample Rate
(kHz)		 Window Length Num Mels Fmax Weights
CNN14 Tagging 43.1 32 1024 64 14k download
CNN14_16k Tagging 43.8 16 512 64 8k download
CNN14_DecisionLevelMax SED 38.5 32 1024 64 14k download

Note: These models will be used as a pretrained model in the fine-tuning tasks below. Check out audioset-tagging-cnn, if you want to train on AudioSet dataset.

Fine-tuned Classification Models
Model Dataset Accuracy
(%)		 Sample Rate
(kHz)		 Weights
CNN14 ESC50 (Fold-5) 95.75 32 download
CNN14 FSDKaggle2018 (test) 93.56 32 download
CNN14 SpeechCommandsv1 (val/test) 96.60/96.77 32 download
Fine-tuned Tagging Models
Model Dataset mAP(%) AUC d-prime Sample Rate
(kHz)		 Config Weights
CNN14 FSDKaggle2019 - - - 32 - -
Fine-tuned SED Models
Model Dataset F1 Sample Rate
(kHz)		 Config Weights
CNN14_DecisionLevelMax DESED - 32 - -

Supported Datasets

Dataset Task Classes Train Val Test Audio Length Audio Spec Size
ESC-50 Classification 50 2,000 5 folds - 5s 44.1kHz, mono 600MB
UrbanSound8k Classification 10 8,732 10 folds - <=4s Vary 5.6GB
FSDKaggle2018 Classification 41 9,473 - 1,600 300ms~30s 44.1kHz, mono 4.6GB
SpeechCommandsv1 Classification 30 51,088 6,798 6,835 <=1s 16kHz, mono 1.4GB
SpeechCommandsv2 Classification 35 84,843 9,981 11,005 <=1s 16kHz, mono 2.3GB
FSDKaggle2019* Tagging 80 4,970+19,815 - 4,481 300ms~30s 44.1kHz, mono 24GB
MTT* Tagging 50 19,000 - - - - 3GB
DESED* SED 10 - - - 10 - -

Notes: * datasets are not available yet. Classification dataset are treated as multi-class/single-label classification and tagging and sed datasets are treated as multi-label classification.

Dataset Structure (click to expand)

Download the dataset and prepare it into the following structure.

datasets
|__ ESC50
    |__ audio

|__ Urbansound8k
    |__ audio

|__ FSDKaggle2018
    |__ audio_train
    |__ audio_test
    |__ FSDKaggle2018.meta
        |__ train_post_competition.csv
        |__ test_post_competition_scoring_clips.csv

|__ SpeechCommandsv1/v2
    |__ bed
    |__ bird
    |__ ...
    |__ testing_list.txt
    |__ validation_list.txt

Augmentations (click to expand)

Currently, the following augmentations are supported. More will be added in the future. You can test the effects of augmentations with this notebook

WaveForm Augmentations:

  • MixUp
  • Background Noise
  • Gaussian Noise
  • Fade In/Out
  • Volume
  • CutMix

Spectrogram Augmentations:

  • Time Masking
  • Frequency Masking
  • Filter Augmentation

Usage

Requirements (click to expand)
  • python >= 3.6
  • pytorch >= 1.8.1
  • torchaudio >= 0.8.1

Other requirements can be installed with pip install -r requirements.txt.


Configuration (click to expand)
  • Create a configuration file in configs. Sample configuration for ESC50 dataset can be found here.
  • Copy the contents of this and then edit the fields you think if it is needed.
  • This configuration file is needed for all of training, evaluation and prediction scripts.

Training (click to expand)

To train with a single GPU:

$ python tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

To train with multiple gpus, set DDP field in config file to true and run as follows:

$ python -m torch.distributed.launch --nproc_per_node=2 --use_env tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

Evaluation (click to expand)

Make sure to set MODEL_PATH of the configuration file to your trained model directory.

$ python tools/val.py --cfg configs/CONFIG_FILE.yaml

Audio Classification/Tagging Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/infer.py --cfg configs/audioset.yaml

You will get an output similar to this:

Class                     Confidence
----------------------  ------------
Speech                     0.897762
Telephone bell ringing     0.752206
Telephone                  0.219329
Inside, small room         0.20761
Music                      0.0770325

Sound Event Detection Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/sed_infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/sed_infer.py --cfg configs/audioset_sed.yaml

You will get an output similar to this:

Class                     Start    End
----------------------  -------  -----
Speech                      2.2    7
Telephone bell ringing      0      2.5

The following plot will also be shown, if you set PLOT to true:

sed_result


References (click to expand)

Citations (click to expand) 
@misc{kong2020panns,
      title={PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition}, 
      author={Qiuqiang Kong and Yin Cao and Turab Iqbal and Yuxuan Wang and Wenwu Wang and Mark D. Plumbley},
      year={2020},
      eprint={1912.10211},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{gong2021ast,
      title={AST: Audio Spectrogram Transformer}, 
      author={Yuan Gong and Yu-An Chung and James Glass},
      year={2021},
      eprint={2104.01778},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{nam2021heavily,
      title={Heavily Augmented Sound Event Detection utilizing Weak Predictions}, 
      author={Hyeonuk Nam and Byeong-Yun Ko and Gyeong-Tae Lee and Seong-Hu Kim and Won-Ho Jung and Sang-Min Choi and Yong-Hwa Park},
      year={2021},
      eprint={2107.03649},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
You might also like...
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Machine learning metrics for distributed, scalable PyTorch applications.

Pytorch Lightning 1.2k Jan 6, 2023
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

null 10 Dec 14, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

null 11.4k Jan 9, 2023
Fast, flexible and easy to use probabilistic modelling in Python.
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 3, 2023
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 2.8k Feb 12, 2021
A fast and easy to use, moddable, Python based Minecraft server!
A fast and easy to use, moddable, Python based Minecraft server!

PyMine PyMine - The fastest, easiest to use, Python-based Minecraft Server! Features Note: This list is not always up to date, and doesn't contain all

PyMine 144 Dec 30, 2022
Releases(v0.2.0)

  • v0.2.0(Aug 17, 2021)

    This release includes the following:

    • Fine-tuned on ESC50, FSDKaggle2018, SpeechCommandsv1
    • Add waveform augmentations
    • Add spectrogram augmentations
    • Add augmentation testing notebook
    • Add tagging metrics
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Aug 13, 2021)

Owner
sithu3
AI Developer
sithu3
GitHub Repository
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Deep Generative Model for Robust Imbalance Classification Deep Generative Model for Robust Imbalance Classification Xinyue Wang, Yilin Lyu, Liping Jin

9 Nov 01, 2022
Implementation of C-RNN-GAN.

Implementation of C-RNN-GAN. Publication: Title: C-RNN-GAN: Continuous recurrent neural networks with adversarial training Information: http://mogren.

Olof Mogren 427 Dec 25, 2022
Council-GAN - Implementation for our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020)

Council-GAN Implementation of our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020) Paper Ori Nizan , Ayellet Tal, Breaking the Cycle

ori nizan 260 Nov 16, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
PRTR: Pose Recognition with Cascade Transformers

PRTR: Pose Recognition with Cascade Transformers Introduction This repository is the official implementation for Pose Recognition with Cascade Transfo

mlpc-ucsd 133 Dec 30, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Introduction PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: Data structure for

Facebook Research 6.8k Jan 01, 2023
Airbus Ship Detection Challenge

Airbus Ship Detection Challenge This is an open solution to the Airbus Ship Detection Challenge. Our goals We are building entirely open solution to t

minerva.ml 55 Nov 29, 2022
Pytorch implementation of Learning Rate Dropout.

Learning-Rate-Dropout Pytorch implementation of Learning Rate Dropout. Paper Link: https://arxiv.org/pdf/1912.00144.pdf Train ResNet-34 for Cifar10: r

42 Nov 25, 2022
Continuum Learning with GEM: Gradient Episodic Memory

Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory

Facebook Research 360 Dec 27, 2022
Reliable probability face embeddings

ProbFace, arxiv This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) me

Kaen Chan 34 Dec 31, 2022
Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

Applicator Kit for Modo Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad with a TrueDepth camera to

Andrew Buttigieg 3 Aug 24, 2021
Conjugated Discrete Distributions for Distributional Reinforcement Learning (C2D)

Conjugated Discrete Distributions for Distributional Reinforcement Learning (C2D) Code & Data Appendix for Conjugated Discrete Distributions for Distr

1 Jan 11, 2022
Reference implementation for Structured Prediction with Deep Value Networks

Deep Value Network (DVN) This code is a python reference implementation of DVNs introduced in Deep Value Networks Learn to Evaluate and Iteratively Re

Michael Gygli 55 Feb 02, 2022
Generative Adversarial Text-to-Image Synthesis

###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee This is the

Scott Ellison Reed 883 Dec 31, 2022
Key information extraction from invoice document with Graph Convolution Network

Key Information Extraction from Scanned Invoices Key information extraction from invoice document with Graph Convolution Network Related blog post fro

Phan Hoang 39 Dec 16, 2022
A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

Sangwoong Yoon 38 Nov 25, 2022
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras. Implementation of various Deep Image Segmentation mo

Divam Gupta 2.6k Jan 05, 2023